Job
- Level
- Experienced
- Job Field
- IT, Data, DevOps
- Employment Type
- Full Time
- Contract Type
- Permanent employment
- Location
- Ingolstadt
- Working Model
- Hybrid, Onsite
Job Summary
In this role, you will architect and maintain a centralized AI platform using Vertex AI, develop automated CI/CD pipelines, and implement safety frameworks for LLMs to ensure compliant AI solutions.
Job Technologies
Your role in the team
- AI Platform Engineering: Architect and maintain a centralized AI platform using Vertex AI, GKE, and BigQuery, ensuring it serves as a robust foundation for company-wide AI initiatives.
- Automated Guardrails & Governance: Design and implement automated safety layers (guardrails) to ensure LLM security, data privacy, and compliance with the EU AI Act.
- Self-Service Tooling: Build and maintain agentic tooling frameworks and "golden paths" that allow internal tech teams to deploy AI agents and RAG systems independently.
- AI / ML-Ops & CI/CD Pipelines: Develop end-to-end automated pipelines (CI/CD/CT) for model training, testing, deployment, and versioning of AI agents.
- Monitoring & Anomaly Detection: Implement advanced observability solutions to monitor model performance, detect drift, and identify system anomalies in real-time.
- Internal Tech Consulting: Act as a technical subject matter expert, advising product and engineering teams on how to architect, integrate, and scale AI solutions within the platform ecosystem.
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Our expectations of you
Qualifications
- Agentic AI Frameworks: Practical knowledge of frameworks like LangChain, LangGraph, Google Agent Development Kit (ADK), and an understanding of how to operationalize agentic workflows at scale.
- Governance & Security: Familiarity with AI safety tools (e.g., NeMo Guardrails) and a mindset for building "secure by design" and compliant AI systems.
- Analytical Mindset: Proven ability to build monitoring for non-deterministic systems, focusing on anomaly detection and performance metrics.
- Communication: Excellent English skills with the ability to translate complex infrastructural requirements into clear documentation and guidance for other developers.
Experience
- Experience: 3-5+ years in Software Engineering or DevOps, with a clear specialization in ML/AI Ops and cloud-scale infrastructure.
- GCP Expertise: Deep hands-on experience with Google Cloud Platform, specifically Vertex AI (Model Registry, Pipelines, Model Monitoring) and BigQuery.
- Technical Stack: Strong proficiency in Python and Infrastructure-as-Code (e.g., Terraform). Experience with Kubernetes (GKE) and Docker.
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Benefits
Work-Life-Integration
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Topics that you deal with on the job
Job Locations
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Description
- Company Type
- Established Company
- Working Model
- Hybrid, Onsite
- Industry
- Electronics, Automatization, Internet, IT, Telecommunication
Dev Reviews
by devworkplaces.com
Total
(1 Review)3.2
Career Growth
3.2Engineering
3.0Workingconditions
3.4Culture
3.2
Experienced AI/ML Ops Engineer
- Location
- Ingolstadt
- Working Model
- Hybrid, Onsite
- Diversity
- Open for all genders
- English Only
- English only required
